A proposed neural network for the integrator of the oculomotor system

Single-unit recordings, stimulation studies, and eye movement measurements all indicate that the firing patterns of many oculomotor neurons in the brain stem encode eye-velocity commands in premotor circuits while the firing patterns of extraocular motoneurons contain both eye-velocity and eye-position components. It is necessary to propose that the eye-position component is generated from the eye-velocity signal by a leaky hold element or temporal integrator. Prior models of this integrator suffer from two important problems. Since cells appear to have a steady, background signal when eye position and velocity are zero, how does the integrator avoid integrating this background rate? Most models employ some form of lumped, oositive feedback the gain of which must be kept within totally unreasonable limits for proper operation. We propose a lateral inhibitory network of homogeneous neurons as a model for the neural integrator that solves both problems. Parameter sensitivity studies and lesion simulations are presented to demonstrate robustness of the model with respect to both the choice of parameter values and the consequences of pathological changes in a portion of the neural integrator pool.     

Extending a biologically inspired model of choice: multi-alternatives, nonlinearity and value-based multidimensional choice

The leaky competing accumulator (LCA) is a biologically inspired model of choice. It describes the processes of leaky accumulation and competition observed in neuronal populations during choice tasks and it accounts for reaction time distributions observed in psychophysical experiments. This paper discusses recent analyses and extensions of the LCA model. First, it reviews the dynamics and examines the conditions that make the model achieve optimal performance. Second, it shows that nonlinearities of the type present in biological neurons improve performance when the number of choice alternatives increases. Third, the model is extended to value-based choice, where it is shown that nonlinearities in the value function explain risk aversion in risky choice and preference reversals in choice between alternatives characterized across multiple dimensions.     

To Punish or to Leave: Distinct Cognitive Processes Underlie Partner Control and Partner Choice Behaviors

When a cooperative partner defects, at least two types of response are available: Punishment, aimed at modifying behavior, and ostracism, aimed at avoiding further social interaction with the partner. These options, termed partner control and partner choice, have been distinguished at behavioral and evolutionary levels. However, little work has compared their cognitive bases. Do these disparate behaviors depend on common processes of moral evaluation? Specifically, we assess whether they show identical patterns of dependence on two key dimensions of moral evaluation: A person’s intentions, and the outcomes that they cause. We address this issue in a “trembling hand” economic game. In this game, an allocator divides a monetary stake between themselves and a responder based on a stochastic mechanism. This allows for dissociations between the allocator’s intent and the actual outcome. Responders were either given the opportunity to punish or reward the allocator (partner control) or to switch to a different partner for a subsequent round of play (partner choice). Our results suggest that partner control and partner choice behaviors are supported by distinct underlying cognitive processes: Partner control exhibits greater sensitivity to the outcomes a partner causes, while partner choice is influenced almost exclusively by a partner’s intentions. This cognitive dissociation can be understood in light of the unique adaptive functions of partner control and partner choice.     

Analytical reconstruction of the neuronal input current from spike train data

The time course of the current driving action potential generation at a neuron investigated experimentally is in general not measurable directly. In this paper an indirect method is introduced that allows estimation of this unknown current time course using only spike train data. Assuming the leaky integrator model as valid for the action potential encoding site of the investigated neuron, the unknown input current is obtained by determining (analytically) a current time course that upon injection into the leaky integrator model evokes action potential sequences identical to those observed experimentally. Applications of this current-reconstruction procedure to neuronal output data obtained from a leaky integrator model showed that the procedure allows a good estimation of the underlying input current even if the membrane time constant of the investigated neuron is not known exactly. Additionally, an application of current reconstruction to experimental data obtained from a cat muscle spindle primary afferent subject to repeated -stimuli is demonstrated.     

Localization Error and Search Costs during Mate Choice in Túngara Frogs,Physalaemus pustulosus

Search costs can have profound influences on female choice, causing females to become less choosy or sample less of the diversity of available mates. Predator foraging strategies, however, determine exactly how search time affects predator encounter rates. Ambush predators are more likely to be encountered by females traveling longer distances to evaluate males, but evaluation time is unlikely to influence encounter‐rate with this type of predator. Actively searching predators, however, may be more likely to be encountered by females employing longer travel times and evaluation times. In this study, we examine the effects of perceived search costs on both temporal and spatial aspects of the search behavior of female túngara frogs, Physalaemus pustulosus. Females were collected from natural choruses and presented with conspecific calls at a distance of 50, 115, or 180 cm from their release point. Assays were conducted in either darkness or simulated full moon light levels. Longer starting distances caused longer choice latencies, but choice latency was considerably lowered under higher light conditions. Females spent considerably less time moving under higher light conditions; however, light levels did not affect path length. Females were more likely to leave the release point with more accurate orientation to the sound source under higher light conditions. We demonstrate that females can respond to perceived search costs by altering spatial and temporal aspects of female search behavior. The overall emphasis of females on reducing time spent moving and increasing movement speed indicates that predation by actively searching predators represents a stronger cost to females than ambush predators.     

Accuracy and response-time distributions for decision-making: linear perfect integrators versus nonlinear attractor-based neural circuits

Animals choose actions based on imperfect, ambiguous data. “Noise” inherent in neural processing adds further variability to this already-noisy input signal. Mathematical analysis has suggested that the optimal apparatus (in terms of the speed/accuracy trade-off) for reaching decisions about such noisy inputs is perfect accumulation of the inputs by a temporal integrator. Thus, most highly cited models of neural circuitry underlying decision-making have been instantiations of a perfect integrator. Here, in accordance with a growing mathematical and empirical literature, we describe circumstances in which perfect integration is rendered suboptimal. In particular we highlight the impact of three biological constraints: (1) significant noise arising within the decision-making circuitry itself; (2) bounding of integration by maximal neural firing rates; and (3) time limitations on making a decision. Under conditions (1) and (2), an attractor system with stable attractor states can easily best an integrator when accuracy is more important than speed. Moreover, under conditions in which such stable attractor networks do not best the perfect integrator, a system with unstable initial states can do so if readout of the system’s final state is imperfect. Ubiquitously, an attractor system with a nonselective time-dependent input current is both more accurate and more robust to imprecise tuning of parameters than an integrator with such input. Given that neural responses that switch stochastically between discrete states can “masquerade” as integration in single-neuron and trial-averaged data, our results suggest that such networks should be considered as plausible alternatives to the integrator model.     

Get It While It’s Hot: A Peak-First Bias in Self-Generated Choice Order in Rhesus Macaques

Animals typically must make a number of successive choices to achieve a goal: e.g., eating multiple food items before becoming satiated. However, it is unclear whether choosing the best first or saving the best for last represents the best choice strategy to maximize overall reward. Specifically, since outcomes can be evaluated prospectively (with future rewards discounted and more immediate rewards preferred) or retrospectively (with prior rewards discounted and more recent rewards preferred), the conditions under which each are used remains unclear. On the one hand, humans and non-human animals clearly discount future reward, preferring immediate rewards to delayed ones, suggesting prospective evaluation; on the other hand, it has also been shown that a sequence that ends well, i.e., with the best event or item last, is often preferred, suggesting retrospective evaluation. Here we hypothesized that when individuals are allowed to build the sequence themselves they are more likely to evaluate each item individually and therefore build a sequence using prospective evaluation. We examined the relationship between self-generated choice order and preference in rhesus monkeys in two experiments in which the distinctiveness of options were relatively high and low, respectively. We observed a positive linear relationship between choice order and preference among highly distinct options, indicating that the rhesus monkeys chose their preferred food first: i.e., a peak-first order preference. Overall, choice order depended on the degree of relative preference among alternatives and a peak-first bias, providing evidence for prospective evaluation when choice order is self-generated.     

Significance of phase-locking measurements as a test for neural encoding models

The significance of phase-locking measurements is discussed by comparing the phase-locking behaviour of three structurally similar neural encoders. They are the integrator with dead time, the self-inhibited integrate-and-fire system with a voltage dependent inhibitory quantum and the (already analysed) leaky integrator. The analysis shows significant differences between these systems; the loss of phaselocking as the input modulation depth increases is typical of the leaky integrator model. Moreover, the analysis performed brings out the importance of the problem of attractiveness in analysing the phaselocking of neural encoder models.     

Repetitive firing: a quantitative study of feedback in model encoders

Recognition of nonlinearities in the neuronal encoding of repetitive spike trains has generated a number of models to explain this behavior. Here we develop the mathematics and a set of tests for two such models: the leaky integrator and the variable-gamma model. Both of these are nearly sufficient to explain the dynamic behavior of a number of repetitively firing, sensory neurons. Model parameters can be related to possible underlying basic mechanisms. Summed and nonsummed, spike- locked negative feedback are examined in conjunction with the models. Transfer functions are formulated to predict responses to steady state, steps, and sinusoidally varying stimuli in which output data are the times of spike-train events only. An electrical analog model for the leaky integrator is tested to verify predicted responses. Curve fitting and parameter variation techniques are explored for the purpose of extracting basic model parameters from spike train data. Sinusoidal analysis of spike trains appear to be a very accurate method for determining spike-locked feedback parameters, and it is to a large extent a model independent method that may also be applied to neuronal responses.     

感染性心内膜炎51例临床分析

目的分析感染性心内膜炎(IE)患者临床特征及疗效,探讨IE的早期诊断及合理治疗。方法回顾性分析2009年1月1日至2010年10月31日收治的51例IE病例的临床资料,分析IE患者的心脏基础疾病情况、血培养、超声心动图改变及治疗效果、预后等。结果 51例IE患者中以无基础疾病(27.45%)及风湿性瓣膜病变后IE(23.50%)最为多见,感染病原体以葡萄球菌属多见(46.88%,15/32),次为链球菌属(28.12%,9/32)。超声心动图检查阳性率高(80.39%),手术治疗21例(41.18%,21/51),51例中共死亡5例。结论 IE的临床特征发生了明显变化,临床需提高认识,多学科合作,使疾病得到恰当治疗处理。     

A Critical Role for the Hippocampus in the Valuation of Imagined Outcomes

Many choice situations require imagining potential outcomes, a capacity that was shown to involve memory brain regions such as the hippocampus. We reasoned that the quality of hippocampus-mediated simulation might therefore condition the subjective value assigned to imagined outcomes. We developed a novel paradigm to assess the impact of hippocampus structure and function on the propensity to favor imagined outcomes in the context of intertemporal choices. The ecological condition opposed immediate options presented as pictures (hence directly observable) to delayed options presented as texts (hence requiring mental stimulation). To avoid confounding simulation process with delay discounting, we compared this ecological condition to control conditions using the same temporal labels while keeping constant the presentation mode. Behavioral data showed that participants who imagined future options with greater details rated them as more likeable. Functional MRI data confirmed that hippocampus activity could account for subjects assigning higher values to simulated options. Structural MRI data suggested that grey matter density was a significant predictor of hippocampus activation, and therefore of the propensity to favor simulated options. Conversely, patients with hippocampus atrophy due to Alzheimer''s disease, but not patients with Fronto-Temporal Dementia, were less inclined to favor options that required mental simulation. We conclude that hippocampus-mediated simulation plays a critical role in providing the motivation to pursue goals that are not present to our senses.     

Modelling neural informational propagation and functional auditory sensory memory with temporal multi-scale operators

In this paper we prove that both diffusion and the leaky integrators cascade based transport mechanisms have as their inherent property the effect of temporal multi-scaling. The two transport mechanisms are modeled not as convolution based algorithms but as causal physical processes. This implies that propagation of information through a neural map may act as a mechanism for achieving temporal multi-scale analysis in the auditory system. Specifically, we are interested in the effects of such a transport process on the formation and the dynamics of auditory sensory memory. Two temporal models of information propagation are discussed and compared in terms of their ability to model auditory sensory memory effects and the biological plausibility of their structure: the causal diffusion based operator (CD) and the leaky integrator cascade based operator (LINC). We show that temporal multi-scale representations achieved by both models exhibit the effects similar to those of auditory sensory memory (filtering, time delay and binding of information). As regards higher-level functions of auditory sensory memory such as change detection, the LINC operator seems to be a biologically more plausible solution for modeling temporal cortical processing.     

A range-normalization model of context-dependent choice: a new model and evidence

Most utility theories of choice assume that the introduction of an irrelevant option (called the decoy) to a choice set does not change the preference between existing options. On the contrary, a wealth of behavioral data demonstrates the dependence of preference on the decoy and on the context in which the options are presented. Nevertheless, neural mechanisms underlying context-dependent preference are poorly understood. In order to shed light on these mechanisms, we design and perform a novel experiment to measure within-subject decoy effects. We find within-subject decoy effects similar to what have been shown previously with between-subject designs. More importantly, we find that not only are the decoy effects correlated, pointing to similar underlying mechanisms, but also these effects increase with the distance of the decoy from the original options. To explain these observations, we construct a plausible neuronal model that can account for decoy effects based on the trial-by-trial adjustment of neural representations to the set of available options. This adjustment mechanism, which we call range normalization, occurs when the nervous system is required to represent different stimuli distinguishably, while being limited to using bounded neural activity. The proposed model captures our experimental observations and makes new predictions about the influence of the choice set size on the decoy effects, which are in contrast to previous models of context-dependent choice preference. Critically, unlike previous psychological models, the computational resource required by our range-normalization model does not increase exponentially as the set size increases. Our results show that context-dependent choice behavior, which is commonly perceived as an irrational response to the presence of irrelevant options, could be a natural consequence of the biophysical limits of neural representation in the brain.     

Effect of delay and social distance on the perceived value of social interaction

Temporal discounting is a process by which the perceived value of an outcome decreases as delay increases. Social discounting is a process by which the perceived value of an outcome decreases as the social distance between the decision-maker and recipient increases. Both temporal and social discounting are well established for monetary outcomes. However, little is known regarding the effect of delay-to-occurrence or increased social distance on the perceived value of social interactions. The current study demonstrates that the decrease in perceived value for delayed social interactions is well described by the same hyperbola-like function used for monetary outcomes and that the rate of discounting decreases (indicating increased preference for larger, more delayed outcomes) as social distance increases. Conceptualizing the current discounting procedure as entailing both potential gains and losses, suggest that under certain circumstances, higher discount rates may not reflect increased preference.     

Repetitive firing: quantitative analysis of encoder behavior of slowly adapting stretch receptor of crayfish and eccentric cell of Limulus

Techniques developed for determining summed encoder feedback in conjunction with the leaky integrator and variable-gamma models for repetitive firing are applied to spike train data obtained from the slowly adapting crustacean stretch receptor and the eccentric cell of Limulus. Input stimuli were intracellularly applied currents. Analysis of data from cells stringently selected by reproducibility criteria gave a consistent picture for the dynamics of repetitive firing. The variable-gamma model with appropriate summed feedback was most accurate for describing encoding behavior of both cell types. The leaky integrator model, while useful for determining summed feedback parameters, was inadequate to account for underlying mechanisms of encoder activity. For the stretch receptor, two summed feedback processes were detected: one had a short time constant; the other, a long one. Appropriate tests indicated that the short time constant effect was from an electrogenic sodium pump, and the same is presumed for the long time constant summed feedback. Both feedbacks show seasonal and/or species variations. Short hyperpolarizing pulses inhibited the feedback from the long time constant process. The eccentric cell also showed two summed feedback processes: one is due to self inhibition, the other is postulated to be a short time constant electrogenic sodium pump similar to that described in the stretch receptor.     

Influence of multiple action–outcome associations on the transition dynamics toward an optimal choice in rats

When faced with familiar versus novel options, animals may exploit the acquired action–outcome associations or attempt to form new associations. Little is known about which factors determine the strategy of choice behavior in partially comprehended environments. Here we examine the influence of multiple action–outcome associations on choice behavior in the context of rewarding outcomes (food) and aversive outcomes (electric foot-shock). We used a nose-poke paradigm with rats, incorporating a dilemma between a familiar option and a novel, higher-value option. In Experiment 1, two groups of rats were trained with different outcome schedules: either a single action–outcome association (“Reward-Only”) or dual action–outcome associations (“Reward-Shock”; with the added opportunity to avoid an electric foot-shock). In Experiment 2, we employed the same paradigm with two groups of rats performing the task under dual action–outcome associations, with different levels of threat (a low- or high-amplitude electric foot-shock). The choice behavior was clearly influenced by the action–outcome associations, with more efficient transition dynamics to the optimal choice with dual rather than single action–outcome associations. The level of threat did not affect the transition dynamics. Taken together, the data suggested that the strategy of choice behavior was modulated by the information complexity of the environment.     

Dynamics of one-dimensional spiking neuron models

In this paper we make a rigorous mathematical analysis of one-dimensional spiking neuron models in a unified framework. We find that, under conditions satisfied in particular by the periodically and aperiodically driven leaky integrator as well as some of its variants, the spike map is increasing on its range, which leaves no room for chaotic behavior. A rigorous expression of the Lyapunov exponent is derived. Finally, we analyse the periodically driven perfect integrator and show that the restriction of the phase map to its range is always conjugated to a rotation, and we provide an explicit expression of the invariant measure.     

Emergence of clusters in the hidden layer of a dynamic recurrent neural network

The neural integrator of the oculomotor system is a privileged field for artificial neural network simulation. In this paper, we were interested in an improvement of the biologically plausible features of the Arnold-Robinson network. This improvement was done by fixing the sign of the connection weights in the network (in order to respect the biological Dale's Law). We also introduced a notion of distance in the network in the form of transmission delays between its units. These modifications necessitated the introduction of a general supervisor in order to train the network to act as a leaky integrator. When examining the lateral connection weights of the hidden layer, the distribution of the weights values was found to exhibit a conspicuous structure: the high-value weights were grouped in what we call clusters. Other zones are quite flat and characterized by low-value weights. Clusters are defined as particular groups of adjoining neurons which have strong and privileged connections with another neighborhood of neurons. The clusters of the trained network are reminiscent of the small clusters or patches that have been found experimentally in the nucleus prepositus hypoglossi, where the neural integrator is located. A study was conducted to determine the conditions of emergence of these clusters in our network: they include the fixation of the weight sign, the introduction of a distance, and a convergence of the information from the hidden layer to the motoneurons. We conclude that this spontaneous emergence of clusters in artificial neural networks, performing a temporal integration, is due to computational constraints, with a restricted space of solutions. Thus, information processing could induce the emergence of iterated patterns in biological neural networks. Received: 18 September 1996 / Accepted in revised form: 7 January 1997     

Bacterial gene expression at low temperatures

Under suboptimal environmental conditions such as low temperatures, many bacteria have an extended lag phase, altered cell structures, and composition such as a less fluid (more rigid) and leaky cytoplasmic membrane. As a result, cells may die, enter into a starvation mode of metabolism or a physiologically viable but non-culturable (VBNC) state. In the latter state, the amount of gene expression per cell is virtually undetectable. In this article, gene expression under (suboptimal) low temperature conditions in non-psychrophilic environmental bacteria is examined. The pros and cons of some of the molecular methodologies for gene expression analysis are also discussed.     

Behavioural correlate of choice confidence in a discrete trial paradigm

How animals make choices in a changing and often uncertain environment is a central theme in the behavioural sciences. There is a substantial literature on how animals make choices in various experimental paradigms but less is known about the way they assess a choice after it has been made in terms of the expected outcome. Here, we used a discrete trial paradigm to characterise how the reward history shaped the behaviour on a trial by trial basis. Rats initiated each trial which consisted of a choice between two drinking spouts that differed in their probability of delivering a sucrose solution. Critically, sucrose was delivered after a delay from the first lick at the spouts--this allowed us to characterise the behavioural profile during the window between the time of choice and its outcome. Rats' behaviour converged to optimum choice, both during the acquisition phase and after the reversal of contingencies. We monitored the post-choice behaviour at a temporal precision of 1 millisecond; lick-response profiles revealed that rats spent more time at the spout with the higher reward probability and exhibited a sparser lick pattern. This was the case when we exclusively examined the unrewarded trials, where the outcome was identical. The differential licking profiles preceded the differential choice ratios and could thus predict the changes in choice behaviour.